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Event memory uniquely predicts memory for large-scale space.

ABSTRACT: When a person explores a new environment, they begin to construct a spatial representation of it. Doing so is important for navigating and remaining oriented. How does one's ability to learn a new environment relate to one's ability to remember experiences in that environment? Here, 208 adults experienced a first-person videotaped route, and then completed a spatial map construction task. They also took tests of general cognitive abilities (working memory, laboratory episodic memory, processing speed, general knowledge) and of memory for familiar, everyday activities (event memory). Regression analyses revealed that event memory (memory for everyday events and their temporal structure), laboratory episodic memory (memory for words and pictures) and gender were unique predictors of spatial memory. These results implicate the processing of temporal structure and organization as an important cognitive ability in large-scale spatial-memory-from-route experience. Accounting for the temporal structure of people's experience while learning the layout of novel spaces may improve interventions for addressing navigation problems.

SUBMITTER: Sargent JQ

PROVIDER: S-EPMC8062070 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Event memory uniquely predicts memory for large-scale space.

Sargent Jesse Q JQ Zacks Jeffrey M JM Hambrick David Z DZ Lin Nan N

Memory & cognition 20190201 2

When a person explores a new environment, they begin to construct a spatial representation of it. Doing so is important for navigating and remaining oriented. How does one's ability to learn a new environment relate to one's ability to remember experiences in that environment? Here, 208 adults experienced a first-person videotaped route, and then completed a spatial map construction task. They also took tests of general cognitive abilities (working memory, laboratory episodic memory, processing ...[more]

PMID: 30229479

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Project description:Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.

Dataset Information

Event memory uniquely predicts memory for large-scale space.

Publications

Event memory uniquely predicts memory for large-scale space.

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